A Beginner's Guide to Power and Energy Measurement and Estimation for Computing and Machine Learning
Akshaya Jagannadharao, Nicole Beckage, Sovan Biswas, Hilary Egan, Jamil Gafur, Thijs Metsch, Dawn Nafus, Giuseppe Raffa, Charles Tripp
TL;DR
The paper addresses the challenge of meaningfully measuring and interpreting energy use in ML workflows across diverse hardware and software environments. It delivers a practical framework—rooted in a simple energy-measurement workflow, multi-level measurement goals, and proxy strategies—for robust, comparable estimates, while highlighting common pitfalls such as idle power baselines, shared-resource effects, and measurement variability. Through scenario walkthroughs and tool guidance, it advocates for standardized reporting and methodological transparency to enable fair cross-system comparisons and informed design choices. The work aims to accelerate energy-aware ML by providing actionable guidance and motivating community standards for measurement and emissions reporting.
Abstract
Concerns about the environmental footprint of machine learning are increasing. While studies of energy use and emissions of ML models are a growing subfield, most ML researchers and developers still do not incorporate energy measurement as part of their work practices. While measuring energy is a crucial step towards reducing carbon footprint, it is also not straightforward. This paper introduces the main considerations necessary for making sound use of energy measurement tools and interpreting energy estimates, including the use of at-the-wall versus on-device measurements, sampling strategies and best practices, common sources of error, and proxy measures. It also contains practical tips and real-world scenarios that illustrate how these considerations come into play. It concludes with a call to action for improving the state of the art of measurement methods and standards for facilitating robust comparisons between diverse hardware and software environments.